Q.U.E.S.T – Five Classic Requirement Analysis Approaches meet GenAI | TTTribeCast Webinar

The challenge isn’t skill. It’s bandwidth.

You know how to analyze requirements. You’ve developed skills and instincts through experience – recognizing where natural language leaves room for interpretation, where implicit assumptions need surfacing, where “the system should handle X securely” carries more ambiguity than the story teller or requirements author intended.

And here’s the constraint: applying rigorous, multi-angle analysis to every requirement indeed takes time. Time that sprints – historically – may not always allow. Time that usually gets squeezed between standups, test execution, status reporting, bug triage, automation and some other important and/or urgent activities.

Requirements are not necessarily broken – natural language is simply ambiguous by nature. 

A tester’s one of the key jobs is extracting clarity from that ambiguity before it propagates into stories, acceptance criteria, code, tests, and production.

Five, time tested, reliable and structured analysis methods exist—Five Whys, 5W1H, Shifting emphasis, Noun-verb extension, and S.M.A.R.T validation. Each surfaces what others miss. Together, they form a realistic approach to extract clarity.

The bottleneck has never been whether these methods work. It’s whether there’s enough bandwidth to apply them consistently.

Experimenting with Q.U.E.S.T

Q.U.E.S.T is an acronym for the integration of five classical requirement analysis methods into one appropriate and potentially repeatable approach for interpreting, analyzing, understanding, and making sense of specified requirements in certain contexts:

1. Question Why
   Drill down to the business purpose using the Five Whys technique.
2. Understand Boundaries
   Map behavioural scope, constraints, and context with 5W1H (What, Why, Who, Where, When, How).
3. Expose Ambiguity
   Stress-test language to uncover interpretation gaps through emphasis shifts.
4. Surface Assumptions
   Identify undefined terms and hidden actions using noun–verb extension.
5. Testability Check
   Ensure requirements are measurable and time-bound using S.M.A.R.T criteria.

Each step builds on the previous. 

What Changes with GenAI

Generative AI can’t replace your Thinking, Skepticism, and Interrogation. All you do is that you choose to delegate the systematic execution of Q.U.E.S.T against specified & documented requirements – while you focus on evaluation, prioritization, and stakeholder conversation.

Think of it as extending your reach, not replacing your thinking.

You stay in control: deciding which findings matter, which questions to escalate, how to surface implicit assumptions. The AI does repetitive pattern-matching. You do the sense-making.

What This Webinar Delivers

In 90 minutes, you’ll gain:

• The Q.U.E.S.T framework – five classical methods integrated into one sequenced approach
• Clear decision logic – why this order, what each step catches that others miss
• Hands-on GenAI integration – how to prompt AI tools to execute Q.U.E.S.T as your analysis assistant
• Live demonstration – a hypothetical ambiguous requirement analyzed, questioned, and clarified using Q.U.E.S.T in real time

Who This Is For

• Testers who want a reliable, repeatable, structured approach to extract clarity from ambiguous requirements
• Testers exploring GenAI who want grounded, practical application—not hype
• Testers who want to multiply their analytical reach without multiplying their hours

What You Don’t Need

• Prior AI or prompt engineering experience
• Paid AI tools—free-tier options are sufficient
• Any belief that AI is magic—healthy skepticism is welcome

Session Format

• Introduction and context setting. 5 minutes
• Interactive core session. 70 minutes
• Q&A and open discussion. 15 minutes

Key Takeaways

1. Q.U.E.S.T proposes a sequenced, structured, and reliable integrated requirement analysis approach leveraging GenAI. 

2. GenAI extends your reach, can’t replace your thinking and judgment. 

3. Utmost clarity at the requirement analysis stage minimizes misunderstanding downstream.